ml_binary_classification_gridsearch_hyperOpt

This repository contains Python code for binary classification using grid search and hyperparameter optimization techniques.

Table of Contents

• ml_binary_classification_gridsearch_hyperOpt
• Overview
• Diagrams
• Getting Started
  • Prerequisites
• Installation
  • Windows
  • Unix/Linux
• Usage
• Examples
• Contributing
• License
• Appendix
• Acknowledgments

Overview

Binary classification is a common machine learning task where the goal is to categorize data into one of two classes. This repository provides a framework for performing binary classification using various machine learning algorithms and optimizing their hyperparameters through grid search and hyperparameter optimization techniques.

Diagrams

Below are visual diagrams representing various components of the project. All .mmd source files are Mermaid diagrams, and the rendered versions are available in .svg or .png formats.

Feature Importance

• Mermaid source
  

Data Pipeline

• Mermaid source
  

Grid Parameter Search Space

• Mermaid source
  

Hyperparameter Search

• Mermaid source
  

Imputation Pipeline

• Mermaid source
  

ML Repository Architecture

• Mermaid source
  

Model Class Listing (Time Series)

• Mermaid source
  

Project Scoring and Model Saving

• Mermaid source
  

Time Series Helper Functions

• Mermaid source
  

Unit Test - Synthetic Data

• Mermaid source
  

Getting Started

Prerequisites

Designed for usage with a numeric data matrix for binary classification. Single or multiple outcome variables (One v rest). An example is provided. Time series is also implemented.

Installation

Windows:

1. Clone the repository:

   git clone https://github.com/SamoraHunter/ml_binary_classification_gridsearch_hyperOpt.git
   cd ml_binary_classification_gridsearch_hyperOpt

2. Run the installation script:

   install.bat

Unix/Linux:

1. Clone the repository:

   git clone https://github.com/SamoraHunter/ml_binary_classification_gridsearch_hyperOpt.git
   cd ml_binary_classification_gridsearch_hyperOpt

2. Run the installation script:

   chmod +x install.sh
   ./install.sh

import sys
sys.path.append('/path/to/ml_grid')
import ml_grid

Usage

See Appendix

Examples

See [ml_grid/tests/unit_test_synthetic.ipynb]

Contributing

If you would like to contribute to this project, please follow these steps:

Fork the repository on GitHub. Create a new branch for your feature or bug fix. Make your changes and commit them with descriptive commit messages. Push your changes to your fork. Create a pull request to the main repository's master branch.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Appendix

Acknowledgments

scikit-learn hyperopt